Natural Language Processing (NLP) in Python for Beginners

NLP: Complete Text Processing with Spacy, NLTK, Scikit-Learn, Deep Learning, word2vec, GloVe, BERT, RoBERTa, DistilBERT

Created by Laxmi Kant | KGP Talkie | 15 hours on-demand video course

Welcome to KGP Talkie’s Natural Language Processing course. It is designed to give you a complete understanding of Text Processing and Mining with the use of State-of-the-Art NLP algorithms in Python. We Learn Spacy and NLTK in details and we will also explore the uses of NLP in real-life. This course covers the basics of NLP to advance topics like BERT, DistilBERT, and FastText. In this course, we will start from level 0 to the advanced level. We will start with basics like what is machine learning and how it works. Thereafter I will take you to Python, Numpy, and Pandas crash course. If you have prior experience you can skip these sections. The real game of NLP will start with Spacy Introduction where I will take you through various steps of NLP preprocessing. We will be using Spacy and NLTK mostly for the text data preprocessing.

What you’ll learn

• Learn complete text processing with Python
• Learn how to extract text from PDF files
• Use Regular Expressions for search in text
• Use SpaCy and NLTK to extract complete text features from raw text
• Use Latent Dirichlet Allocation for Topic Modelling
• Use Scikit-Learn and Deep Learning for Text Classification
• Learn Multi-Class and Multi-Label Text Classification
• Use Spacy and NLTK for Sentiment Analysis
• Understand and Build word2vec and GloVe based ML models
• Use Gensim to obtain pretrained word vectors and compute similarities and analogies
• Learn Text Summarization and Text Generation using LSTM and GRU

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